Business Intelligence (BI) generally refers to software tools used to improve business enterprise decision-making. These tools are commonly applied to financial, human resource, marketing, sales, customer and supplier analyses. More specifically, these tools can include: reporting and analysis tools to present information, content delivery infrastructure systems for delivery and management of reports and analytics, data warehousing systems for cleansing and consolidating information from disparate sources, and data management systems such as relational databases or On Line Analytic Processing (OLAP) systems used to collect, store, and manage raw data.
In recent years, BI tools have permeated business information systems to the point where the reliability, scalability, and flexibility of BI tools directly impacts the operational efficiency of enterprise business processes. Business users expect Quick access to a wide variety of customized BI tools that provide a rich feature set. This creates a need for locally developed BI tools that are executed against local data sources. This leads to users demanding local BI systems.
At the same time, organizations require some uniformity in decision making. This is a challenge when an organization spans multiple sites. Local system evolution, if not controlled, creates data and tool versioning problems. There is a need for bi-directional communication of both data and BI tools to address these problems. However, there is also a need to eliminate setup time for BI applications or the elements that support a BI activity ported between sites.
In view of the foregoing, it would be highly advantageous to provide improved cluster technology. In particular, it would be highly advantageous to provide an improved cluster technology for the efficient distribution of BI tools and data.